10) Consider the following partial computer output from a simple linear regression analysis.VariableInterceptXCoefficient-28.131.12Std. Deviation319.660.04891t statistic-0.08822.895According to the results, please conclude the hypothesis testing for the slope(Ho:B₁0 vs. Ha:B₁ # 0).p-value0.93090.0001

The regression shows a relationship b/w 1 dependent variable and 1 or more independent variable.

Answered: 10) Consider the following partial…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Consider the following Stata regression output (some values are deliberately removed). Variable | Obs Mean Std. Dev. Min Маx lwage points | rebounds | assists 269 6.952296 .8813761 5.010635 8.655214 269 10.21041 5.900667 1.2 29.8 269 4.401115 2.892573 2.092986 .5 17.3 269 269 2.408922 1682.193 12.6 3533 minutes | 893.3278 33 Source | SS df MS Number of obs F(, Prob > F Model | Residual | = R-squared Adj R-squared Root MSE %3D 0.4146 Total | lwage | Сoef. Std. Err. P>|t| [95% Conf. Interval .0795364 points rebounds .0277761 .0637763 .0204514 3.12 0.002 0.252 0.227 -.0230647 -.0000747 .087425 .0003133 assists .0321805 .0280576 1.15 minutes .0001193 .0000985 1.21 _cons Answer the following questions. Please round your answers to 2 decimal places. Model SS : ; Residual S : ; Total SS :
41
Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude Temperature a. Find the explained variation. (Round to two decimal places as needed.) 2 55 8 40 13 25 20 - 3 28 - 26 31 - 41 34 - 53
A multiple regression analysis produced the following tables. Predictor Coefficients StandardErrort Statistic p-valueIntercept -139.609 2548.989 -0.05477 0.957154x 24.24619 22.25267 1.089586 0.295682x 32.10171 17.44559 1.840105 0.08869Source df SS MS F p-valueRegression 2 302689 151344.5 1.705942 0.219838Residual 13 1153309 88716.07Total 15 1455998Using = 0.01 to test the null hypothesis H :?1 = ?2 = 0, the critical F value is ____.6.701.964.845.995.70
The table below lists measured amounts of redshift and the distances (billions of light-years) to randomly selected astronomical objects. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 90% confidence level with a redshift of 0.0126. Find the explained variation. Redshift Distance OA, 0.157904 OB. 0.444904 OC. 0.000582 OD. 0.219582 0.0231 0.34 0.0536 0.76 0.0715 0.99 0.0391 0.56 0.0438 0.63 0.0109 0.15
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Coefficients 1186 213.48 Coefficients - 130.46 294.11 Standard Error Does the sign of the coefficient for the variable safety rating make sense? 123.87 21.89 14.26 356.37 t Stat P-value 9.575 0.0000 9.752 0.0000 -9.149 0.0000 0.825 0.4128
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Answer Coefficients 933 167.94 - 102.63 -199.18 Does the sign of the coefficient for the variable credit score make sense? Coefficients Standard Error 95.65 17.99 10.89 19.98 t Stat P-value 9.754 0.0000 9.335 0.0000 -9.424 0.0000 -9.969 0.0000 O Yes, because it is expected that as the credit score increases then the cost should decrease. O No, because it is expected that as the credit score increases then the cost should decrease. O Yes, because it is expected that as the credit score increases then the cost should also increase. O No, because it is expected that as the credit score increases then the cost should also increase. Tables Keypad Keyboard Shortcuts
Let's study the relationship between brand, camera resolution, and internal storage capacity on the price of smartphones. Use α = .05 to perform a regression analysis of the Smartphones01CS dataset, and then answer the following questions. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text. a. Did you find any evidence of multicollinearity and variance inflation among the predictors. Explain your answer using a VIF analysis. b. Copy and paste the normal probability plot for your analysis. Is there any evidence that the errors are not normally distributed? Explain. c. Copy and paste the Residuals vs. Predicted Y-values. Does the pattern support the null hypothesis of constant variance for the errors? Explain. d. Study the residuals analysis. Which observations, if any, have unusual residuals? e. Study the residuals analysis. Calculate the leverage statistic. Which observations, if any, are high leverage…
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Coefficients Coefficients Standard Error t Stat P-value Intercept 956 97.23 9.832 0.0000 Car Accidents 172.08 18.24 9.434 0.0000 (In last 3 years) Credit Score Safety Rating 105.16 201.03 0.523 0.6030 -207.81 20.46 - 10.157 0.0000 Does the sign of the coefficient for the variable credit score make sense? Answer No, because it is expected that as the credit score increases then the cost should decrease. ○ No, because it is expected that as the credit score increases then the cost should also increase. ○ Yes, because it is expected that as the credit score increases then the cost should decrease. ○ Yes, because it is expected that as the credit score increases then the cost should also increase. Tables Keypad Keyboard Shortcuts
i)Test individually whether the slope coefficients are significant at 10% significance level. ii)Test the overall significance of the estimated regression at 1% significance level.
Police sometimes use footprint evidence to estimate the height of a suspect. Data was collected from 36 randomly selected men in Nebraska in 2001. The regression model assumptions have been checked, and they are all satisfied. We want to test if there is a positive linear association between shoe print size and average height. The p-value of the appropriate test was found to be 0.0006. (a) Give your brief conclusion. Note that no significance level is provided. (b) Give your conclusion in the context of the problem. (This question will be marked manually by your instructor).

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